Reglerteknik (Automatic Control)

Machine Learning

Machine Learning

PhD course, 2013

General Information

Data is becoming more and more widely available and the world is now in a situation where there
is more data than we can handle. This clearly calls for new technology and this challenge has
resulted in the rapid growth of the machine learning area over the past decade. This course provides
an introduction into the area of machine learning, focusing on dynamical systems. To a large extent
this involves probabilistic modeling in order to be able to solve a wide range of problems.

Contents

Linear regression

Linear classification

Neural networks

Support vector machines

Expectation Maximization (EM)

Clustering

Approximate inference (VB and EP)

Graphical models

Boosting

Sampling methods and MCMC

Bayesian nonparametric (BNP) models

Organization and Examination

The course gives 9 hp (you can receive an additional 3 hp by carrying out a project).

Lectures: 11

The examination consists in a standard written 3 day (72 h) exam. The exam period is March 17 -
April 26, 2013.